Cheap data! Stanford scientists’ “opposites attract” algorithm plunders public databases, scores surprising drug-disease hook-ups
Depending on how you stroke the numbers, it costs anywhere between $500 million and $1 billion to develop a new drug from scratch. So the idea of finding new uses for old drugs, called “drug repositioning,” is appealing.
By applying an “opposites attract” algorithm to publicly available gene-expression databases, Stanford’s Atul Butte, MD, PhD, and his colleagues have come up with numerous such potential matches, a pair of new studies shows. Any drug that tended to move the activity levels of genes within a tissue in one direction, while a disease moved them in the opposite direction, became a candidate to test against that disease.
The researchers showed, in proof-of-concept preclinical studies, that two of the drugs fingered by their molecular Match.com work. (Cimetidine, an ulcer drug, showed promise as a possible remedy for lung adenocarcinoma; likewise with topiramate, a seizure drug, for Crohn’s disease.)
There are all kinds of potential matches between existing drugs and diseases for which those drugs have never even been thought worth considering, much less tested in clinical trials. But sometimes drug developers stumble on such applications, as our news release on the study notes:
To name one popular example, a compound originally developed for heart problems turned out to be effective for erectile dysfunction and, eventually, for a severe lung disorder called pulmonary hypertension. That drug, sildenafil, is more commonly known by its brand name, Viagra.
What is new is discovering these relationships bya systematic search rather than by accident.
Photo by Courtney Carmody